Model-based reinforcement learning for protein backbone design
This work addresses the challenge of designing protein nanomaterials with predefined shapes for potential medical applications, representing an incremental improvement in protein design methods.
The paper tackles the problem of efficiently exploring protein fitness landscapes for optimal protein backbone design by proposing the use of AlphaZero with a novel threshold-based reward and secondary objectives, resulting in protein backbones that better respect structural scores and consistently surpass baseline MCTS by more than 100% in top-down design tasks.
Designing protein nanomaterials of predefined shape and characteristics has the potential to dramatically impact the medical industry. Machine learning (ML) has proven successful in protein design, reducing the need for expensive wet lab experiment rounds. However, challenges persist in efficiently exploring the protein fitness landscapes to identify optimal protein designs. In response, we propose the use of AlphaZero to generate protein backbones, meeting shape and structural scoring requirements. We extend an existing Monte Carlo tree search (MCTS) framework by incorporating a novel threshold-based reward and secondary objectives to improve design precision. This innovation considerably outperforms existing approaches, leading to protein backbones that better respect structural scores. The application of AlphaZero is novel in the context of protein backbone design and demonstrates promising performance. AlphaZero consistently surpasses baseline MCTS by more than 100% in top-down protein design tasks. Additionally, our application of AlphaZero with secondary objectives uncovers further promising outcomes, indicating the potential of model-based reinforcement learning (RL) in navigating the intricate and nuanced aspects of protein design